The human epidermal growth factor receptor 2 (HER2) is a critical protein in the growth of cancer cells, and its expression level is a vital indicator of breast cancer aggressiveness. Traditionally, HER2 status is assessed manually by pathologists examining immunohistochemically (IHC) stained tissue slides. This manual process, however, is often subject to inter- and intra-observer variability and can be time-consuming, leading to potential delays in diagnosis and treatment planning.
Deep learning approach enhances HER2 scoring in breast cancer
